{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can press *shift + enter* to quickly advance through each line of a notebook. Try it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that you have a recent version of TensorFlow installed, v1.3 or higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "print(\"You have version %s\" % tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if Matplotlib is working. After running this cell, you should see a plot appear below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pylab\n",
    "import numpy as np\n",
    "\n",
    "# create some data using numpy. y = x * 0.1 + 0.3 + noise\n",
    "x = np.random.rand(100).astype(np.float32)\n",
    "noise = np.random.normal(scale=0.01, size=len(x))\n",
    "y = x * 0.1 + 0.3 + noise\n",
    "\n",
    "# plot it\n",
    "pylab.plot(x, y, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if Numpy and Pillow are working. After runnign this cell, you should see a random image appear below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import PIL.Image as Image\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "image_array = np.random.rand(200,200,3) * 255\n",
    "img = Image.fromarray(image_array.astype('uint8')).convert('RGBA')\n",
    "imshow(np.asarray(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if Pandas is working. After running this cell, you should see a table appear below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "names = ['Bob','Jessica','Mary','John','Mel']\n",
    "births = [968, 155, 77, 578, 973]\n",
    "BabyDataSet = list(zip(names,births))\n",
    "pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You're ready to start the workshop."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
